---
title: "Who cites Beyond Chinchilla-Optimal: Accounting for…"
description: "47 papers of the Elamur Research library cite «Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Beyond Chinchilla-Optimal: Accounting for…](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/) / cited by

# Who cites «Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws»

47 papers of our library cite it (of the 140,884 whose bibliographies we read) · 10 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)

1. [**Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters**Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.*2024 · 524 citations*](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/)
2. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
3. [**MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies**Shengding Hu, Yuge Tu, Xu Han, et al.*2024 · 258 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minicpm-unveiling-potential-small-language/)
4. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/)
5. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
6. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/)
7. [**Distillation Scaling Laws**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
8. [**Resolving Discrepancies in Compute-Optimal Scaling of Language Models**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/)
9. [**Chinchilla Scaling: A replication attempt**Tamay Besiroglu, Ege Erdil, Matthew Barnett, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/besiroglu-2024-chinchilla-scaling-replication-attempt/)
10. [**Scaling Laws for Code: A More Data-Hungry Regime**Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/luo-2026-scaling-laws-code-more-data/)

37 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/) — Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws